




from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__a							     =					299_792_458

# Symbols
__a     ,__a     ,__a     ,__a							     =					symbols('ct x y z')







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if velocity > c:
          raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!''' )
     elif velocity < 1:
          # Usually the speed should be much higher than 1 (c order of magnitude)
          raise ValueError('''Speed must be greater than or equal to 1!''' )

     return velocity / c







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     return 1 / sqrt(1 - beta(_lowercase ) ** 2 )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     return np.array(
         [
             [gamma(_lowercase ), -gamma(_lowercase ) * beta(_lowercase ), 0, 0],
             [-gamma(_lowercase ) * beta(_lowercase ), gamma(_lowercase ), 0, 0],
             [0, 0, 1, 0],
             [0, 0, 0, 1],
         ] )







def lowerCamelCase__  (     _lowercase					,		_lowercase = None ):

     '''simple docstring'''


     if event is None:
          UpperCAmelCase_     :				Optional[Any]  =     np.array([ct, x, y, z] )  # Symbolic four vector
     else:
          event[0] *= c  # x0 is ct (speed of light * time)

     return transformation_matrix(_lowercase ) @ event


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Example of symbolic vector:
     __a							     =					transform(29_979_245)
     print('Example of four vector: ')
     print(F"""ct' = {four_vector[0]}""")
     print(F"""x' = {four_vector[1]}""")
     print(F"""y' = {four_vector[2]}""")
     print(F"""z' = {four_vector[3]}""")

     # Substitute symbols with numerical values
     __a							     =					{ct: c, x: 1, y: 1, z: 1}
     __a							     =					[four_vector[i].subs(sub_dict) for i in range(4)]

     print(F"""\n{numerical_vector}""")





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_					=   {
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["DebertaTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "DebertaForMaskedLM",
      "DebertaForQuestionAnswering",
      "DebertaForSequenceClassification",
      "DebertaForTokenClassification",
      "DebertaModel",
      "DebertaPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFDebertaForMaskedLM",
      "TFDebertaForQuestionAnswering",
      "TFDebertaForSequenceClassification",
      "TFDebertaForTokenClassification",
      "TFDebertaModel",
      "TFDebertaPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
  from .tokenization_deberta import DebertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_fast import DebertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta import (
        DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
        DebertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta import (
        TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
        TFDebertaPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



